Bumping up version number and updating docs in prep for release (#143)

* added cate estimator to causal estimator and one specific estiamtor for linear regression

* Started with a better error message for estimators that do not support CATE as yet, but then generalized to add error messages for when any specific method is not implemented for an estimator.

* added docstrings for the new functionality

* added a warning if effect modifiers provided are extra from the ones specified before, and fixed a bug for discrete variables

* updated files in prep for release of v0.4

Makefile

@@ -3,7 +3,7 @@ init:
 test:
 	py.test tests
 check: 
-	python setup.py checka
+	python setup.py check
 sdist:
 	python setup.py sdist
 jupyter:

docs/source/code_repo.rst

@@ -1,19 +1,53 @@
 Code repository & Versions
-=================
+==========================
 
 DoWhy is hosted on GitHub.
 
 You can browse the code in a html-friendly format `here
 <https://github.com/Microsoft/dowhy>`_.
 
-Version 0.2-alpha (WIP)
------------------------
-This version includes breaking changes.
 
-* The CausalModel import is now simpler. `from dowhy import CausalModel`.
-* do_why.py is renamed to causal_model.py.  This should not change any external
-  usage.
+v0.4-beta: Powerful refutations and better support for heterogeneous treatment effects
+--------------------------------------------------------------------------------------
 
-Version 0.1.1-alpha
--------------------
+* DummyOutcomeRefuter now includes machine learning functions to increase power of the refutation.
+	* In addition to generating a random dummy outcome, now you can generate a dummyOutcome that is an arbitrary function of confounders but always independent of treatment, and then test whether the estimated treatment effect is zero. This is inspired by ideas from the T-learner.
+	* We also provide default machine learning-based methods to estimate such a dummyOutcome based on confounders. Of course, you can specify any custom ML method.
+
+* Added a new BootstrapRefuter that simulates the issue of measurement error with confounders. Rather than a simple bootstrap, you can generate bootstrap samples with noise on the values of the confounders and check how sensitive the estimate is.
+	* The refuter supports custom selection of the confounders to add noise to.
+
+* All refuters now provide confidence intervals and a significance value.
+
+* Better support for heterogeneous effect libraries like EconML and CausalML
+	* All CausalML methods can be called directly from DoWhy, in addition to all methods from EconML.
+	* [Change to naming scheme for estimators] To achieve a consistent naming scheme for estimators, we suggest to prepend internal dowhy estimators with the string "dowhy". For example, "backdoor.dowhy.propensity_score_matching". Not a breaking change, so you can keep using the old naming scheme too.
+	* EconML-specific: Since EconML assumes that effect modifiers are a subset of confounders, a warning is issued if a user specifies effect modifiers outside of confounders and tries to use EconML methods.
+
+* CI and Standard errors: Added bootstrap-based confidence intervals and standard errors for all methods. For linear regression estimator, also implemented the corresponding parametric forms.
+
+* Convenience functions for getting confidence intervals, standard errors and conditional treatment effects (CATE), that can be called after fitting the estimator if needed
+
+* Better coverage for tests. Also, tests are now seeded with a random seed, so more dependable tests.
+
+Thanks to @Tanmay-Kulkarni101 and @Arshiaarya  for their contributions!
+
+v0.2-alpha: CATE estimation and integration with EconML
+-------------------------------------------------------
+
+
+This release includes many major updates:
+
+* (BREAKING CHANGE) The CausalModel import is now simpler: "from dowhy import CausalModel"
+*  Multivariate treatments are now supported. 
+*  Conditional Average Treatment Effects (CATE) can be estimated for any subset of the data. Includes integration with EconML--any method from EconML can be called using DoWhy through the estimate_effect method (see example notebook). 
+*  Other than CATE, specific target estimands like ATT and ATC are also supported for many of the estimation methods.   
+* For reproducibility, you can specify a random seed for all refutation methods. 
+* Multiple bug fixes and updates to the documentation.
+
+
+Includes contributions from @j-chou, @ktmud, @jrfiedler, @shounak112358, @Lnk2past. Thank you all!
+
+v0.1.1-alpha: First release
+---------------------------
 This is the first release of the library.

docs/source/conf.py

@@ -20,7 +20,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'DoWhy | Making Causal Inference Easy'
-copyright = '2018, Microsoft'
+copyright = '2020, Microsoft'
 author = 'Amit Sharma, Emre Kiciman'
 
 # The short X.Y version

docs/source/dowhy.causal_estimators.rst

@@ -4,10 +4,18 @@ dowhy.causal\_estimators package
 Submodules
 ----------
 
-dowhy.causal\_estimators.econml\_cate\_estimator module
--------------------------------------------------------
+dowhy.causal\_estimators.causalml module
+----------------------------------------
 
-.. automodule:: dowhy.causal_estimators.econml_cate_estimator
+.. automodule:: dowhy.causal_estimators.causalml
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+dowhy.causal\_estimators.econml module
+--------------------------------------
+
+.. automodule:: dowhy.causal_estimators.econml
    :members:
    :undoc-members:
    :show-inheritance:
@@ -28,6 +36,14 @@ dowhy.causal\_estimators.linear\_regression\_estimator module
    :undoc-members:
    :show-inheritance:
 
+dowhy.causal\_estimators.propensity\_score\_estimator module
+------------------------------------------------------------
+
+.. automodule:: dowhy.causal_estimators.propensity_score_estimator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 dowhy.causal\_estimators.propensity\_score\_matching\_estimator module
 ----------------------------------------------------------------------
 

docs/source/dowhy.causal_refuters.rst

@@ -12,6 +12,14 @@ dowhy.causal\_refuters.add\_unobserved\_common\_cause module
    :undoc-members:
    :show-inheritance:
 
+dowhy.causal\_refuters.bootstrap\_refuter module
+------------------------------------------------
+
+.. automodule:: dowhy.causal_refuters.bootstrap_refuter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 dowhy.causal\_refuters.data\_subset\_refuter module
 ---------------------------------------------------
 
@@ -20,6 +28,14 @@ dowhy.causal\_refuters.data\_subset\_refuter module
    :undoc-members:
    :show-inheritance:
 
+dowhy.causal\_refuters.dummy\_outcome\_refuter module
+-----------------------------------------------------
+
+.. automodule:: dowhy.causal_refuters.dummy_outcome_refuter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 dowhy.causal\_refuters.placebo\_treatment\_refuter module
 ---------------------------------------------------------
 

docs/source/dowhy.rst

@@ -5,6 +5,7 @@ Subpackages
 -----------
 
 .. toctree::
+   :maxdepth: 4
 
    dowhy.api
    dowhy.causal_estimators

dowhy/VERSION

@@ -1 +1 @@
-0.2
+0.4

setup.py

@@ -31,21 +31,19 @@
     license='MIT',
     long_description=long_description,
     url='https://github.com/microsoft/dowhy',  # Optional
-    download_url='https://github.com/microsoft/dowhy/archive/v0.2.tar.gz',
+    download_url='https://github.com/microsoft/dowhy/archive/v0.4.tar.gz',
     author='Amit Sharma, Emre Kiciman',
     classifiers=[  # Optional
-        'Development Status :: 3 - Alpha',
+        'Development Status :: 4 - Beta',
         'License :: OSI Approved :: MIT License',
-
-        'Programming Language :: Python :: 3.4',
-        'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
     ],
 
     keywords='causality machine-learning causal-inference statistics graphical-model',
     packages=find_packages(exclude=['docs', 'tests']),
-    python_requires='>=3.4',
+    python_requires='>=3.5',
     install_requires=install_requires,
     include_package_data=True,
     package_data={'dowhy':['VERSION']}